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A Lightweight Attention-Enhanced CNN-SVM Hybrid Approach for Maize Leaf Disease Classification:

MD Soyeb Hoque1, Gunjan Basak1, Dipan Deb1, Nikhil Sah1, Arghyasree Saha1 [0009-0006-5879-6445] and Pawan Kumar Singh1,* [0000-0002-9598-7981]
1 Department of Information Technology, Jadavpur University, Salt Lake Campus, Kolkata-700106, India

{hoquesoyeb9@gmail.com, gunjanbasak007@gmail.com, deb.dipan2004@gmail.com, nikhil.jadavpur@gmail.com, arghyasree07@gmail.com, pawansingh.ju@gmail.com}
*Corresponding author: Pawan Kumar Singh {pawansingh.ju@gmail.com}

Maize productivity is threatened by leaf diseases such as curvularia leaf spot, small spot, and rust, making early detection essential. Manual diagnosis is labor-intensive and error-prone,
while many deep learning (DL) models are computationally demanding for practical deployment. This study introduces a lightweight, attention-enhanced hybrid framework that combines
MobileNetV2 with a Convolutional Block Attention Module (CBAM) for discriminative feature extraction along with a Support Vector Machine (SVM) for robust disease classification.
Extensive experiments on two benchmark datasets demonstrate classification accuracies of 98.11% and 99.44%. In addition, a comprehensive ablation study is performed to analyze
the impact of attention mechanism and hybrid learning, confirming their effectiveness in improving performance and generalization. Overall, the results highlight the proposed framework’s
efficiency and suitability for real-world agricultural applications.